A soft-threshold filtering approach for reconstruction from a limited number of projections

In the medical imaging field, discrete gradient transform (DGT) is widely used as a sparsifying operator to define the total variation (TV). Recently, TV minimization has become a hot topic in image reconstruction and is usually implemented using the steepest descent method (SDM). Since TV minimization with the SDM takes a long computational time, here we construct a pseudo-inverse of the DGT and adapt a soft-threshold filtering algorithm, whose convergence and efficiency have been theoretically proven. Also, we construct a pseudo-inverse of the discrete difference transform (DDT) and design an algorithm for L1 minimization of the total difference. These two methods are evaluated in numerical simulation. The results demonstrate the merits of the proposed techniques.